Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu in 1996. It is a density-based clustering non-parametric algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points th…
DBSCAN - Density-Based Spatial Clustering of Applications with Noise. Finds core samples of high density and expands clusters from them. Good for data which contains clusters of similar density. Read more in the User Guide. The maximum distance between two samples for one to be considered as in the neighborhood of the other.
DBSCAN algorithm identifies the dense region by grouping together data points that are closed to each other based on distance measurement. Python implementation of above algorithm without using the sklearn library can be found here dbscan_in_python .
Here we will focus on Density-based spatial clustering of applications with noise (DBSCAN) clustering method. Clusters are dense regions in the data space, separated by regions of the lower density of points. The DBSCAN algorithm is based on this intuitive notion of “clusters” and “noise”.
Noise Point (z): Data point that has no core points within epsilon (ε) distance. DBSCAN is very sensitive to the values of epsilon and minPoints. Therefore, it is important to understand how to select the values of epsilon and minPoints.